Based on Raghabendra Chattopadhyay and Esther Duflo. 2004. Women as Policy Makers: Evidence from a Randomized Policy Experiment in India, Econometrica, 72(5): 1409–43.
All materials presented here build on the resources for instructors designed by Elena Llaudet and Kosuke Imai in Data Analysis for Social Science: A Friendly and Practical Introduction (Princeton University Press).
Let’s continue working with the data from the experiment in India. As a reminder, Table 1 shows the names and descriptions of the variables in this dataset, where the unit of observation is villages.
Table 1: Variables in “india.csv”
| Variable | Description |
|---|---|
| village | village identifier (“Gram Panchayat number_village number”) |
| female | whether village was assigned a female politician: 1=yes, 0=no |
| water | number of new (or repaired) drinking water facilities in the village since random assignment |
| irrigation | number of new (or repaired) irrigation facilities in the village since random assignment |
In this problem set, we will practice how to compute and interpret means and other summary statistics.
As always, we will start by loading and looking at the data (don’t forget to set your working directory first!):
village female water irrigation
1 GP1_village2 1 10 0
2 GP1_village1 1 0 5
3 GP2_village2 1 2 2
4 GP2_village1 1 31 4
5 GP3_village2 0 0 0
6 GP3_village1 0 0 0Produce a frequency table which reports how many of the villages in the sample have female politicians and how many have male politicians, and interpret the results provided. Note: use the count() function and remember to install and load the tidyverse set of packages first, if required.
Produce a table of proportions which reports the percentage of villages in the sample that have female politicians and male politicians, and interpret the results provided. Note: use the count() function in combination with mutate().
Create a histogram which shows the distribution of the variable irrigation and explain what this tells us. Hint: you will first need to install and load the ggplot2 package.
Compute the mean, median, standard deviation and variation of the variable irrigation and interpret these figures. Please use the summarise() command to obtain your answers.